short-paper

Code2graph: automatic generation of static call graphs for Python source code

Authors:

Gharib Gharibi,

Rashmi Tripathi,

Yugyung LeeAuthors Info & Claims

ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

Pages 880 - 883

https://doi.org/10.1145/3238147.3240484

Published: 03 September 2018 Publication History

Abstract

A static call graph is an imperative prerequisite used in most interprocedural analyses and software comprehension tools. However, there is a lack of software tools that can automatically analyze the Python source-code and construct its static call graph. In this paper, we introduce a prototype Python tool, named code2graph, which automates the tasks of (1) analyzing the Python source-code and extracting its structure, (2) constructing static call graphs from the source code, and (3) generating a similarity matrix of all possible execution paths in the system. Our goal is twofold: First, assist the developers in understanding the overall structure of the system. Second, provide a stepping stone for further research that can utilize the tool in software searching and similarity detection applications. For example, clustering the execution paths into a logical workflow of the system would be applied to automate specific software tasks. Code2graph has been successfully used to generate static call graphs and similarity matrices of the paths for three popular open-source Deep Learning projects (TensorFlow, Keras, PyTorch). A tool demo is available at https://youtu.be/ecctePpcAKU.

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Cited By

Cao YChen LChen ZZhong JZhang XWang L(2024)Efficient Construction of Practical Python Call Graphs with Entity Knowledge BaseInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450010434:07(999-1024)Online publication date: 22-May-2024
https://doi.org/10.1142/S0218194024500104
Li CPei YShen YLu JFan YLinghu XTian YWang K(2024)PyVisVue3D3: Python visualization from hierarchy tree to call graphSoftwareX10.1016/j.softx.2024.10168926(101689)Online publication date: May-2024
https://doi.org/10.1016/j.softx.2024.101689
Fedorchenko ENovikova EFedorchenko AVerevkin S(2023)An Analytical Review of the Source Code Models for Exploit AnalysisInformation10.3390/info1409049714:9(497)Online publication date: 8-Sep-2023
https://doi.org/10.3390/info14090497
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Index Terms

Code2graph: automatic generation of static call graphs for Python source code
1. Software and its engineering
  1. Software organization and properties
    1. Software functional properties
      1. Formal methods
        Automated static analysis

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Published In

cover image ACM Conferences

ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

September 2018

955 pages

ISBN:9781450359375

DOI:10.1145/3238147

General Chair:
Marianne Huchard
University of Montpellier, France
,
Program Chairs:
Christian Kästner
Carnegie Mellon University, USA
,
Gordon Fraser
University of Passau, Germany

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence
CNRS: Centre National De La Rechercue Scientifique
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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Association for Computing Machinery

New York, NY, United States

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Published: 03 September 2018

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ASE '18: 33rd ACM/IEEE International Conference on Automated Software Engineering

September 3 - 7, 2018

Montpellier, France

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Cited By

Cao YChen LChen ZZhong JZhang XWang L(2024)Efficient Construction of Practical Python Call Graphs with Entity Knowledge BaseInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450010434:07(999-1024)Online publication date: 22-May-2024
https://doi.org/10.1142/S0218194024500104
Li CPei YShen YLu JFan YLinghu XTian YWang K(2024)PyVisVue3D3: Python visualization from hierarchy tree to call graphSoftwareX10.1016/j.softx.2024.10168926(101689)Online publication date: May-2024
https://doi.org/10.1016/j.softx.2024.101689
Fedorchenko ENovikova EFedorchenko AVerevkin S(2023)An Analytical Review of the Source Code Models for Exploit AnalysisInformation10.3390/info1409049714:9(497)Online publication date: 8-Sep-2023
https://doi.org/10.3390/info14090497
Zhao ZWang XXu ZTang ZLi YDi PLiu AMuccini H(2023)Incremental Call Graph Construction in Industrial PracticeProceedings of the 45th International Conference on Software Engineering: Software Engineering in Practice10.1109/ICSE-SEIP58684.2023.00048(471-482)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1109/ICSE-SEIP58684.2023.00048
Chimalakonda SDas DMathai ATamilselvam SKumar A(2023)The Landscape of Source Code Representation Learning in AI-Driven Software Engineering Tasks2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)10.1109/ICSE-Companion58688.2023.00098(342-343)Online publication date: May-2023
https://doi.org/10.1109/ICSE-Companion58688.2023.00098
Liu DFeng YYan YXu B(2023)Towards understanding bugs in Python interpretersEmpirical Software Engineering10.1007/s10664-022-10239-x28:1Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s10664-022-10239-x
Venigalla ABoyalakuntla KChimalakonda SRastogi ATufano RBavota GArnaoudova VHaiduc S(2022)GitQ- towards using badges as visual cues for GitHub projectsProceedings of the 30th IEEE/ACM International Conference on Program Comprehension10.1145/3524610.3527876(157-161)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3524610.3527876
Bhattacharjee ARoy BSchneider KPasquale LTreude C(2022)Supporting program comprehension by generating abstract code summary treeProceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results10.1145/3510455.3512793(81-85)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510455.3512793
Abadi AMakovitzki BShemer RTyszberowicz SHong JBures MPark JCerny T(2022)A lightweight approach for sound call graph approximationProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507126(1837-1844)Online publication date: 6-May-2022
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Bhattacharjee ARoy BSchneider K(2022)Supporting program comprehension by generating abstract code summary tree2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)10.1109/ICSE-NIER55298.2022.9793515(81-85)Online publication date: May-2022
https://doi.org/10.1109/ICSE-NIER55298.2022.9793515
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